{"title":"片断增长混合模型的贝叶斯方法:学校心理学中的问题与应用","authors":"Ihnwhi Heo, Sarah Depaoli, Fan Jia, Haiyan Liu","doi":"10.1016/j.jsp.2024.101366","DOIUrl":null,"url":null,"abstract":"<div><div>Bayesian piecewise growth mixture models (PGMMs) are a powerful statistical tool based on the Bayesian framework for modeling nonlinear, phasic developmental trajectories of heterogeneous subpopulations over time. Although Bayesian PGMMs can benefit school psychology research, their empirical applications within the field remain limited. This article introduces Bayesian PGMMs, addresses three key methodological considerations (i.e., class separation, class enumeration, and prior sensitivity), and provides practical guidance for their implementation. By analyzing a dataset from the Early Childhood Longitudinal Study-Kindergarten Cohort, we illustrate the application of Bayesian PGMMs to model piecewise growth trajectories of mathematics achievement across latent classes. We underscore the importance of considering both statistical criteria and substantive theories when making decisions in analytic procedures. Additionally, we discuss the importance of transparent reporting of the results and provide caveats for researchers in the field to promote the wide usage of Bayesian PGMMs.</div></div>","PeriodicalId":48232,"journal":{"name":"Journal of School Psychology","volume":null,"pages":null},"PeriodicalIF":3.8000,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Bayesian approach to piecewise growth mixture modeling: Issues and applications in school psychology\",\"authors\":\"Ihnwhi Heo, Sarah Depaoli, Fan Jia, Haiyan Liu\",\"doi\":\"10.1016/j.jsp.2024.101366\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Bayesian piecewise growth mixture models (PGMMs) are a powerful statistical tool based on the Bayesian framework for modeling nonlinear, phasic developmental trajectories of heterogeneous subpopulations over time. Although Bayesian PGMMs can benefit school psychology research, their empirical applications within the field remain limited. This article introduces Bayesian PGMMs, addresses three key methodological considerations (i.e., class separation, class enumeration, and prior sensitivity), and provides practical guidance for their implementation. By analyzing a dataset from the Early Childhood Longitudinal Study-Kindergarten Cohort, we illustrate the application of Bayesian PGMMs to model piecewise growth trajectories of mathematics achievement across latent classes. We underscore the importance of considering both statistical criteria and substantive theories when making decisions in analytic procedures. Additionally, we discuss the importance of transparent reporting of the results and provide caveats for researchers in the field to promote the wide usage of Bayesian PGMMs.</div></div>\",\"PeriodicalId\":48232,\"journal\":{\"name\":\"Journal of School Psychology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.8000,\"publicationDate\":\"2024-09-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of School Psychology\",\"FirstCategoryId\":\"102\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0022440524000864\",\"RegionNum\":1,\"RegionCategory\":\"心理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"PSYCHOLOGY, SOCIAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of School Psychology","FirstCategoryId":"102","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0022440524000864","RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PSYCHOLOGY, SOCIAL","Score":null,"Total":0}
Bayesian approach to piecewise growth mixture modeling: Issues and applications in school psychology
Bayesian piecewise growth mixture models (PGMMs) are a powerful statistical tool based on the Bayesian framework for modeling nonlinear, phasic developmental trajectories of heterogeneous subpopulations over time. Although Bayesian PGMMs can benefit school psychology research, their empirical applications within the field remain limited. This article introduces Bayesian PGMMs, addresses three key methodological considerations (i.e., class separation, class enumeration, and prior sensitivity), and provides practical guidance for their implementation. By analyzing a dataset from the Early Childhood Longitudinal Study-Kindergarten Cohort, we illustrate the application of Bayesian PGMMs to model piecewise growth trajectories of mathematics achievement across latent classes. We underscore the importance of considering both statistical criteria and substantive theories when making decisions in analytic procedures. Additionally, we discuss the importance of transparent reporting of the results and provide caveats for researchers in the field to promote the wide usage of Bayesian PGMMs.
期刊介绍:
The Journal of School Psychology publishes original empirical articles and critical reviews of the literature on research and practices relevant to psychological and behavioral processes in school settings. JSP presents research on intervention mechanisms and approaches; schooling effects on the development of social, cognitive, mental-health, and achievement-related outcomes; assessment; and consultation. Submissions from a variety of disciplines are encouraged. All manuscripts are read by the Editor and one or more editorial consultants with the intent of providing appropriate and constructive written reviews.